Abstract
Programming heterogeneous parallel computer systems is notoriously difficult, but MIMD models have proven to be portable across multi-core processors, clusters, and massively parallel systems. It would be highly desirable for GPUs (Graphics Processing Units) also to be able to leverage algorithms and programming tools designed for MIMD targets. Unfortunately, most GPU hardware implements a very restrictive multi-threaded SIMD-based execution model.
This paper presents a compiler, assembler, and interpreter system that allows a GPU to implement a richly featured MIMD execution model that supports shared-memory communication, recursion, etc. Through a variety of careful design choices and optimizations, reasonable efficiency is obtained on NVIDIA CUDA GPUs. The discussion covers both the methods used and the motivation in terms of the relevant aspects of GPU architecture.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
NVIDIA, NVIDIA CUDA compute unified device architecture programming guide version 1.0 (June 2007)
ATI, ATI stream SDK user guide v1.3-beta (December 2008)
ClearSpeed. ClearSpeed whitepaper: CSX processor architecture, ClearSpeed Technology plc, vol. PN-1110-0702 (2007)
Blank, T.: The maspar mp-1 architecture. In: 35th IEEE Computer Society International Conference (COMPCON) (February 1990)
Wilsey, P., Hensgen, D., Slusher, C., Abu-Ghazaleh, N., Hollinden, D.: Exploiting simd computers for mutant program execution, Technical Report No. TR 133-11- 91, Department of Electrical and Computer Engineering, University of Cincinnati, Cincinnati, Ohio (November 1991)
Dietz, H.G., Cohen, W.E.: A massively parallel mimd implemented by SIMD hardware, Purdue University School of Electrical Engineering Technical Report TR-EE 92-4, 28 pages (January 1992)
Thinking Machines Corporation, Connection machine model cm-2 technical sum- mary, version 5.1 (May 1989)
Siegel, H., Nation, W., Allemang, M.: The organization of the PASM: Reconfigurable parallel processing system. In: Ohio State Parallel Computing Workshop, March 1990, pp. 1–12 (1990)
Nilsson, M., Tanaka, H.: MIMD Execution by SIMD Computers. Journal of Information Processing. Information Processing Society of Japan 13(1), 58–61 (1990)
Langdon, W.B., Banzhaf, W.: A SIMD interpreter for genetic programming on GPU graphics cards. In: O’Neill, M., Vanneschi, L., Gustafson, S., Esparcia Alcazar, A.I., De Falco, I., Della Cioppa, A., Tarantino, E. (eds.) EuroGP 2008. LNCS, vol. 4971, pp. 73–85. Springer, Heidelberg (2008)
Dietz, H.G., Cohen, W.E.: A control-parallel programming model implemented on simd hardware. In: Banerjee, U., Gelernter, D., Nicolau, A., Padua, D. (eds.) LCPC 1993. LNCS, vol. 768, pp. 96–114. Springer, Heidelberg (1994)
Abu-ghazaleh, N.B., Wilsey, P.A., Fan, X., Hensgen, D.A.: Synthesizing variable instruction issue interpreters for implementing functional parallelism on SIMD computers. IEEE Transactions on Parallel and Distributed Systems (1997)
Khronos OpenCL Working Group, The OpenCL specification version 1.0 (December 2008)
Lipchak, B., et al.: Arb fragment program, OpenGL Extension Registry (August 2002), http://oss.sgi.com/projects/ogl-sample/registry/ARB/fragment_program.txt
Pixel Shader Reference, http://msdn.microsoft.com/archive/default.asp?url=/archive/en-us/dx81_c/directx_cpp/graphics/reference/shader/pixel/pixel.asp
Huffman, D.A.: A method for the construction of minimum-redundancy codes. Proceedings of the IRE 40(9), 1098–1101 (1952)
Dietz, H.G.: Common subexpression induction. In: 1992 International Conference on Parallel Processing, Saint Charles, Illinois, August 1992, vol. II (1992)
Hou, Q., Zhou, K., Guo, A.: Debugging gpu stream programs through automatic data ow recording and visualization (May 2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dietz, H.G., Young, B.D. (2010). MIMD Interpretation on a GPU. In: Gao, G.R., Pollock, L.L., Cavazos, J., Li, X. (eds) Languages and Compilers for Parallel Computing. LCPC 2009. Lecture Notes in Computer Science, vol 5898. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13374-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-13374-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13373-2
Online ISBN: 978-3-642-13374-9
eBook Packages: Computer ScienceComputer Science (R0)